Associations of stay-at-home order and face-masking recommendation with trends in daily new cases and deaths of laboratory-confirmed COVID-19 in the United States

2020 
Background and objectives Public health interventions have reduced coronavirus disease 2019 (COVID-19) transmission in several countries, but their impacts on COVID-19 epidemics in the USA are unclear. We examined associations of stay-at-home order (SAHO) and face-masking recommendation with COVID-19 epidemics in the USA. Methods In this quasi-experimental interrupted time-series study, we modeled temporal trends in daily new cases and deaths of laboratory-confirmed COVID-19 cases, and COVID-19 time-varying reproduction numbers in the USA between March 1 and April 20, 2020. In addition, we conducted simulation analyses. Results The number of residents under SAHO increased since March 19 and plateaued at 290,829,980 (88.6% of the U.S. population) on April 7. Trends in COVID-19 time-varying reproduction numbers peaked on March 23, further reduced on April 3, and fell below/around 1.0 on April 13. Early-implementation and early-lift of SAHO would reduce and increase COVID-19 epidemics, respectively. Multivariable piecewise log-linear regression revealed the states' neighboring relationship with New York was linked to COVID-19 daily new cases and deaths. There were two turning points in daily new-case trend, being March 28 (slope-changes = -0.09) and April 3 (slope-changes = -0.09), which appeared to be associated with implementation of SAHO on March 28 (affecting 48.5% of the US population in 22 states and District of Columbia), and face-masking recommendation on April 3, respectively. There were also two turning points in daily new-death trend, being April 9 (slope-changes = -0.06) and April 19 (slope-changes = -0.90). Conclusions We identified two turning points of COVID-19 daily new cases or deaths in the USA, which seem to be linked to implementation of SAHO and the Center for Disease Control's face-masking recommendation.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    26
    References
    16
    Citations
    NaN
    KQI
    []
    Baidu
    map